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stringlengths 13
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|---|---|---|---|
122264561/cell_99 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_react_datetime = df_react.set_index('Datetime').drop('Unnamed: 0', axis=1)
counts = df_react.groupby(pd.Grouper(key='Datetime', freq='M')).count()
freq_df = pd.DataFrame(counts['Type'])
freq_df | code |
122264561/cell_55 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
df_cont_score = df_cont_score.reset_index()
df_cont_score | code |
122264561/cell_76 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content.isnull().sum()
df_content.groupby('Type')['Type_score'].mean()
df_content | code |
122264561/cell_92 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_react | code |
122264561/cell_91 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_profile = df_profile.drop('Unnamed: 0', axis=1)
df_profile2 = df_profile.explode('Interests')
df_profile2 | code |
122264561/cell_65 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content['type_sentiment'].value_counts() | code |
122264561/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment | code |
122264561/cell_73 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content.isnull().sum()
df_content.groupby('Type')['Type_score'].mean()
df_content | code |
122264561/cell_61 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0) | code |
122264561/cell_72 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content.isnull().sum()
df_content.groupby('Type')['Type_score'].mean() | code |
122264561/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react['Type'].value_counts() | code |
122264561/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_content['Category'].value_counts() | code |
122264561/cell_69 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content['type_sentiment'].value_counts() / 10 | code |
122264561/cell_86 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_profile = df_profile.drop('Unnamed: 0', axis=1)
df_profile | code |
122264561/cell_52 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
df_cont_score | code |
122264561/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
122264561/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react['Content ID'].value_counts() | code |
122264561/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react.isnull().sum() | code |
122264561/cell_82 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content.isnull().sum()
df_content.groupby('Type')['Type_score'].mean()
data = df_content['Category'].value_counts().index
data = data[:19]
index = df_content[df_content['Category'].isin(data)].groupby('Category')['Type_score'].mean().sort_values().index[:19]
plt.figure(figsize=(12, 6), dpi=200)
sns.barplot(x='Category', y='Type_score', data=df_content[df_content['Category'].isin(data)], order=index)
plt.xticks(rotation=90)
plt.xlabel('Category')
plt.ylabel('Reaction Score')
plt.title('Content Category vs Reaction Score')
plt.savefig('Content Category vs Mean Reaction Score.jpeg', bbox_inches='tight') | code |
122264561/cell_51 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_sentiment | code |
122264561/cell_62 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content | code |
122264561/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_rtypes2 = df_rtypes[['Type', 'Score']]
df_rtypes2 = df_rtypes2.set_index('Type')
my_map = df_rtypes2.to_dict().get('Score')
my_map | code |
122264561/cell_95 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_react_datetime = df_react.set_index('Datetime').drop('Unnamed: 0', axis=1)
df_react_datetime | code |
122264561/cell_80 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content.isnull().sum()
df_content.groupby('Type')['Type_score'].mean()
data = df_content['Category'].value_counts().index
data = data[:19]
data | code |
122264561/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react['Datetime'].dt.date.max() | code |
122264561/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react['Datetime'] | code |
122264561/cell_66 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content['type_sentiment'].value_counts() | code |
122264561/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react | code |
122264561/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react.isnull().sum()
df_react[df_react['Type_score'].isnull()].isnull().sum() | code |
122264561/cell_77 | [
"text_plain_output_1.png"
] | code |
|
122264561/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_react['type_sentiment'].value_counts() / 245.73 | code |
122264561/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
mode_counts | code |
122264561/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react['Datetime'].dt.date.min() | code |
122264561/cell_97 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_react_datetime = df_react.set_index('Datetime').drop('Unnamed: 0', axis=1)
counts = df_react.groupby(pd.Grouper(key='Datetime', freq='M')).count()
counts['Type'] | code |
122264561/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react['User ID'].value_counts() | code |
122264561/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
my_map2 = df_rtypes.set_index('Type').to_dict().get('Sentiment')
my_map2 | code |
122264561/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_react.isnull().sum()
df_react = df_react.dropna()
df_react | code |
122264561/cell_71 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content.isnull().sum()
plt.figure(figsize=(10, 6), dpi=200)
sns.countplot(x='type_sentiment', data=df_content)
plt.savefig('Mode Reaction Distribution Of Content.jpeg') | code |
122264561/cell_70 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rtypes = pd.read_csv('/kaggle/input/a360-internship-practice/ReactionTypes.csv')
df_react = pd.read_csv('/kaggle/input/a360-internship-practice/Reactions.csv')
df_session = pd.read_csv('/kaggle/input/a360-internship-practice/Session.csv')
df_user = pd.read_csv('/kaggle/input/a360-internship-practice/User.csv')
df_react['Datetime'] = pd.to_datetime(df_react['Datetime'])
df_content = df_content.drop(['Unnamed: 0'], axis=1)
df_react.isnull().sum()
df_react = df_react.dropna()
df_react_users = df_react.drop(['Unnamed: 0', 'Content ID', 'Type'], axis=1)
df_cont_score = pd.DataFrame(df_react.groupby('Content ID')['Type_score'].mean())
mode_counts = df_react.groupby('Content ID')['type_sentiment'].agg(lambda x: x.mode().iloc[0])
selected_mode = mode_counts.index[0]
df_cont_sentiment = pd.DataFrame(mode_counts)
df_cont_sentiment = df_cont_sentiment.reset_index()
df_cont_score = df_cont_score.reset_index()
df_content = pd.merge(df_content, df_cont_score, how='outer')
df_content = pd.merge(df_content, df_cont_sentiment, how='outer')
df_content.fillna(0)
df_content.isnull().sum() | code |
122264561/cell_85 | [
"application_vnd.jupyter.stderr_output_1.png"
] | df.explode('my_list_column') | code |
325724/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import os
from sklearn.preprocessing import LabelEncoder
from scipy.sparse import csr_matrix, hstack
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import StratifiedKFold
from sklearn.metrics import log_loss | code |
325724/cell_7 | [
"text_plain_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.preprocessing import LabelEncoder
import numpy as np
import os
import pandas as pd
datadir = '../input'
gatrain = pd.read_csv(os.path.join(datadir, 'gender_age_train.csv'), index_col='device_id')
gatest = pd.read_csv(os.path.join(datadir, 'gender_age_test.csv'), index_col='device_id')
phone = pd.read_csv(os.path.join(datadir, 'phone_brand_device_model.csv'))
phone = phone.drop_duplicates('device_id', keep='first').set_index('device_id')
events = pd.read_csv(os.path.join(datadir, 'events.csv'), parse_dates=['timestamp'], index_col='event_id')
appevents = pd.read_csv(os.path.join(datadir, 'app_events.csv'), usecols=['event_id', 'app_id', 'is_active'], dtype={'is_active': bool})
applabels = pd.read_csv(os.path.join(datadir, 'app_labels.csv'))
labelcats = pd.read_csv(os.path.join(datadir, 'label_categories.csv'), index_col='label_id', squeeze=True)
gatrain['trainrow'] = np.arange(gatrain.shape[0])
gatest['testrow'] = np.arange(gatest.shape[0])
brandencoder = LabelEncoder().fit(phone.phone_brand)
phone['brand'] = brandencoder.transform(phone['phone_brand'])
gatrain['brand'] = phone['brand']
gatest['brand'] = phone['brand']
Xtr_brand = csr_matrix((np.ones(gatrain.shape[0]), (gatrain.trainrow, gatrain.brand)))
Xte_brand = csr_matrix((np.ones(gatest.shape[0]), (gatest.testrow, gatest.brand)))
print('Brand features: train shape {}, test shape {}'.format(Xtr_brand.shape, Xte_brand.shape)) | code |
325724/cell_8 | [
"text_plain_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.preprocessing import LabelEncoder
import numpy as np
import os
import pandas as pd
datadir = '../input'
gatrain = pd.read_csv(os.path.join(datadir, 'gender_age_train.csv'), index_col='device_id')
gatest = pd.read_csv(os.path.join(datadir, 'gender_age_test.csv'), index_col='device_id')
phone = pd.read_csv(os.path.join(datadir, 'phone_brand_device_model.csv'))
phone = phone.drop_duplicates('device_id', keep='first').set_index('device_id')
events = pd.read_csv(os.path.join(datadir, 'events.csv'), parse_dates=['timestamp'], index_col='event_id')
appevents = pd.read_csv(os.path.join(datadir, 'app_events.csv'), usecols=['event_id', 'app_id', 'is_active'], dtype={'is_active': bool})
applabels = pd.read_csv(os.path.join(datadir, 'app_labels.csv'))
labelcats = pd.read_csv(os.path.join(datadir, 'label_categories.csv'), index_col='label_id', squeeze=True)
gatrain['trainrow'] = np.arange(gatrain.shape[0])
gatest['testrow'] = np.arange(gatest.shape[0])
brandencoder = LabelEncoder().fit(phone.phone_brand)
phone['brand'] = brandencoder.transform(phone['phone_brand'])
gatrain['brand'] = phone['brand']
gatest['brand'] = phone['brand']
Xtr_brand = csr_matrix((np.ones(gatrain.shape[0]), (gatrain.trainrow, gatrain.brand)))
Xte_brand = csr_matrix((np.ones(gatest.shape[0]), (gatest.testrow, gatest.brand)))
m = phone.phone_brand.str.cat(phone.device_model)
modelencoder = LabelEncoder().fit(m)
phone['model'] = modelencoder.transform(m)
gatrain['model'] = phone['model']
gatest['model'] = phone['model']
Xtr_model = csr_matrix((np.ones(gatrain.shape[0]), (gatrain.trainrow, gatrain.model)))
Xte_model = csr_matrix((np.ones(gatest.shape[0]), (gatest.testrow, gatest.model)))
print('Model features: train shape {}, test shape {}'.format(Xtr_model.shape, Xte_model.shape)) | code |
325724/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.sparse import csr_matrix, hstack
from sklearn.preprocessing import LabelEncoder
import numpy as np
import os
import pandas as pd
datadir = '../input'
gatrain = pd.read_csv(os.path.join(datadir, 'gender_age_train.csv'), index_col='device_id')
gatest = pd.read_csv(os.path.join(datadir, 'gender_age_test.csv'), index_col='device_id')
phone = pd.read_csv(os.path.join(datadir, 'phone_brand_device_model.csv'))
phone = phone.drop_duplicates('device_id', keep='first').set_index('device_id')
events = pd.read_csv(os.path.join(datadir, 'events.csv'), parse_dates=['timestamp'], index_col='event_id')
appevents = pd.read_csv(os.path.join(datadir, 'app_events.csv'), usecols=['event_id', 'app_id', 'is_active'], dtype={'is_active': bool})
applabels = pd.read_csv(os.path.join(datadir, 'app_labels.csv'))
labelcats = pd.read_csv(os.path.join(datadir, 'label_categories.csv'), index_col='label_id', squeeze=True)
gatrain['trainrow'] = np.arange(gatrain.shape[0])
gatest['testrow'] = np.arange(gatest.shape[0])
brandencoder = LabelEncoder().fit(phone.phone_brand)
phone['brand'] = brandencoder.transform(phone['phone_brand'])
gatrain['brand'] = phone['brand']
gatest['brand'] = phone['brand']
Xtr_brand = csr_matrix((np.ones(gatrain.shape[0]), (gatrain.trainrow, gatrain.brand)))
Xte_brand = csr_matrix((np.ones(gatest.shape[0]), (gatest.testrow, gatest.brand)))
m = phone.phone_brand.str.cat(phone.device_model)
modelencoder = LabelEncoder().fit(m)
phone['model'] = modelencoder.transform(m)
gatrain['model'] = phone['model']
gatest['model'] = phone['model']
Xtr_model = csr_matrix((np.ones(gatrain.shape[0]), (gatrain.trainrow, gatrain.model)))
Xte_model = csr_matrix((np.ones(gatest.shape[0]), (gatest.testrow, gatest.model)))
appencoder = LabelEncoder().fit(appevents.app_id)
appevents['app'] = appencoder.transform(appevents.app_id)
napps = len(appencoder.classes_)
deviceapps = appevents.merge(events[['device_id']], how='left', left_on='event_id', right_index=True).groupby(['device_id', 'app'])['app'].agg(['size']).merge(gatrain[['trainrow']], how='left', left_index=True, right_index=True).merge(gatest[['testrow']], how='left', left_index=True, right_index=True).reset_index()
d = deviceapps.dropna(subset=['trainrow'])
Xtr_app = csr_matrix((np.ones(d.shape[0]), (d.trainrow, d.app)), shape=(gatrain.shape[0], napps))
d = deviceapps.dropna(subset=['testrow'])
Xte_app = csr_matrix((np.ones(d.shape[0]), (d.testrow, d.app)), shape=(gatest.shape[0], napps))
print('Apps data: train shape {}, test shape {}'.format(Xtr_app.shape, Xte_app.shape)) | code |
327693/cell_13 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import tensorflow as tf
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
train_val_ratio = 0.7
train_data_size = len(train_data)
train_set = train_data[:int(train_data_size * train_val_ratio)]
val_set = train_data[int(train_data_size * train_val_ratio) + 1:]
init = tf.initialize_all_variables()
saver = tf.train.Saver()
sess = tf.Session()
sess.run(init)
train_eval_list = []
val_eval_list = []
for i in range(100):
batch = train_set.sample(frac=0.1)
batch_xs = batch.drop('label', axis=1).as_matrix() / 255.0
batch_ys = pd.get_dummies(batch['label']).as_matrix()
val_xs = val_set.drop('label', axis=1).as_matrix() / 255.0
val_ys = pd.get_dummies(val_set['label']).as_matrix()
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
train_eval = sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys})
val_eval = sess.run(accuracy, feed_dict={x: val_xs, y_: val_ys})
train_eval_list.append(train_eval)
val_eval_list.append(val_eval) | code |
327693/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
f, axarr = plt.subplots(10, 10)
for row in range(10):
for column in range(10):
entry = train_data[train_data['label']==column].iloc[row].drop('label').as_matrix()
axarr[row, column].imshow(entry.reshape([28, 28]))
axarr[row, column].get_xaxis().set_visible(False)
axarr[row, column].get_yaxis().set_visible(False)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
train_val_ratio = 0.7
train_data_size = len(train_data)
train_set = train_data[:int(train_data_size * train_val_ratio)]
val_set = train_data[int(train_data_size * train_val_ratio) + 1:]
init = tf.initialize_all_variables()
saver = tf.train.Saver()
sess = tf.Session()
sess.run(init)
train_eval_list = []
val_eval_list = []
for i in range(100):
batch = train_set.sample(frac=0.1)
batch_xs = batch.drop('label', axis=1).as_matrix() / 255.0
batch_ys = pd.get_dummies(batch['label']).as_matrix()
val_xs = val_set.drop('label', axis=1).as_matrix() / 255.0
val_ys = pd.get_dummies(val_set['label']).as_matrix()
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
train_eval = sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys})
val_eval = sess.run(accuracy, feed_dict={x: val_xs, y_: val_ys})
train_eval_list.append(train_eval)
val_eval_list.append(val_eval)
plt.plot(train_eval_list, label='train set')
plt.plot(val_eval_list, label='validation set')
ax.set_xlabel('Epoch')
ax.set_ylabel('Accuracy')
plt.legend() | code |
327693/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import tensorflow as tf
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
train_val_ratio = 0.7
train_data_size = len(train_data)
train_set = train_data[:int(train_data_size * train_val_ratio)]
val_set = train_data[int(train_data_size * train_val_ratio) + 1:]
init = tf.initialize_all_variables()
saver = tf.train.Saver()
sess = tf.Session()
sess.run(init)
train_eval_list = []
val_eval_list = []
for i in range(100):
batch = train_set.sample(frac=0.1)
batch_xs = batch.drop('label', axis=1).as_matrix() / 255.0
batch_ys = pd.get_dummies(batch['label']).as_matrix()
val_xs = val_set.drop('label', axis=1).as_matrix() / 255.0
val_ys = pd.get_dummies(val_set['label']).as_matrix()
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
train_eval = sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys})
val_eval = sess.run(accuracy, feed_dict={x: val_xs, y_: val_ys})
train_eval_list.append(train_eval)
val_eval_list.append(val_eval)
saver.save(sess, 'logistic_regression.ckpt')
sess.close() | code |
327693/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
print(train_data.shape)
print(test_data.shape) | code |
327693/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
f, axarr = plt.subplots(10, 10)
for row in range(10):
for column in range(10):
entry = train_data[train_data['label'] == column].iloc[row].drop('label').as_matrix()
axarr[row, column].imshow(entry.reshape([28, 28]))
axarr[row, column].get_xaxis().set_visible(False)
axarr[row, column].get_yaxis().set_visible(False) | code |
48165682/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.isnull().sum()
df = df.dropna()
df_shows = df[df['type'] == 'TV Show']
df_movies = df[df['type'] == 'Movie']
df_date = df_shows[['date_added']].dropna()
df_date['year'] = df_date['date_added'].apply(lambda x: x.split(', ')[-1])
df_date['month'] = df_date['date_added'].apply(lambda x: x.lstrip().split(' ')[0])
month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'][::-1]
dfu = df_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
plt.pcolor(dfu, cmap='afmhot_r', edgecolors='white', linewidths=2)
plt.xticks(np.arange(0.5, len(dfu.columns), 1), dfu.columns, fontsize=7, fontfamily='serif')
plt.yticks(np.arange(0.5, len(dfu.index), 1), dfu.index, fontsize=7, fontfamily='serif')
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=8)
cbar.ax.minorticks_on()
df['date_added'] = pd.to_datetime(df['date_added'])
df['day_added'] = df['date_added'].dt.day
df['year_added'] = df['date_added'].dt.year
df['month_added'] = df['date_added'].dt.month
df['year_added'].astype(int)
df['day_added'].astype(int)
f,ax=plt.subplots(1,2,figsize=(18,8))
df['type'].value_counts().plot.pie(explode=[0,0.1],autopct='%1.1f%%',ax=ax[0],shadow=True)
ax[0].set_title('Type of Movie')
ax[0].set_ylabel('Count')
sns.countplot('type',data=df,ax=ax[1],order=df['type'].value_counts().index)
ax[1].set_title('Count of Source')
plt.show()
f, ax = plt.subplots(1, 2, figsize=(18, 8))
df['rating'].value_counts().plot.pie(autopct='%1.1f%%', ax=ax[0], shadow=True)
ax[0].set_title('Movie Rating')
ax[0].set_ylabel('Count')
sns.countplot('rating', data=df, ax=ax[1], order=df['rating'].value_counts().index)
ax[1].set_title('Count of Rating')
plt.show() | code |
48165682/cell_4 | [
"image_output_1.png"
] | !pip install plotly
!pip install cufflinks
!pip install textblob | code |
48165682/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
48165682/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.isnull().sum()
df = df.dropna()
df['date_added'] = pd.to_datetime(df['date_added'])
df['day_added'] = df['date_added'].dt.day
df['year_added'] = df['date_added'].dt.year
df['month_added'] = df['date_added'].dt.month
df['year_added'].astype(int)
df['day_added'].astype(int)
df.head() | code |
48165682/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.isnull().sum()
df = df.dropna()
df_shows = df[df['type'] == 'TV Show']
df_movies = df[df['type'] == 'Movie']
df_date = df_shows[['date_added']].dropna()
df_date['year'] = df_date['date_added'].apply(lambda x: x.split(', ')[-1])
df_date['month'] = df_date['date_added'].apply(lambda x: x.lstrip().split(' ')[0])
month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'][::-1]
dfu = df_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
plt.pcolor(dfu, cmap='afmhot_r', edgecolors='white', linewidths=2)
plt.xticks(np.arange(0.5, len(dfu.columns), 1), dfu.columns, fontsize=7, fontfamily='serif')
plt.yticks(np.arange(0.5, len(dfu.index), 1), dfu.index, fontsize=7, fontfamily='serif')
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=8)
cbar.ax.minorticks_on()
df['date_added'] = pd.to_datetime(df['date_added'])
df['day_added'] = df['date_added'].dt.day
df['year_added'] = df['date_added'].dt.year
df['month_added'] = df['date_added'].dt.month
df['year_added'].astype(int)
df['day_added'].astype(int)
f,ax=plt.subplots(1,2,figsize=(18,8))
df['type'].value_counts().plot.pie(explode=[0,0.1],autopct='%1.1f%%',ax=ax[0],shadow=True)
ax[0].set_title('Type of Movie')
ax[0].set_ylabel('Count')
sns.countplot('type',data=df,ax=ax[1],order=df['type'].value_counts().index)
ax[1].set_title('Count of Source')
plt.show()
f,ax=plt.subplots(1,2,figsize=(18,8))
df['rating'].value_counts().plot.pie(autopct='%1.1f%%',ax=ax[0],shadow=True)
ax[0].set_title('Movie Rating')
ax[0].set_ylabel('Count')
sns.countplot('rating',data=df,ax=ax[1],order=df['rating'].value_counts().index)
ax[1].set_title('Count of Rating')
plt.show()
group_country_movies = df.groupby('country')['show_id'].count().sort_values(ascending=False).head(10)
plt.ioff()
group_country_movies = df.groupby('year_added')['show_id'].count().sort_values(ascending=False).head(10)
plt.subplots(figsize=(15, 8))
group_country_movies.plot('bar', fontsize=12, color='blue')
plt.xlabel('Number of Movies', fontsize=12)
plt.ylabel('Year', fontsize=12)
plt.title('Movie Count By Year', fontsize=12)
plt.ioff() | code |
48165682/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.isnull().sum()
df = df.dropna()
df_shows = df[df['type'] == 'TV Show']
df_movies = df[df['type'] == 'Movie']
df_date = df_shows[['date_added']].dropna()
df_date['year'] = df_date['date_added'].apply(lambda x: x.split(', ')[-1])
df_date['month'] = df_date['date_added'].apply(lambda x: x.lstrip().split(' ')[0])
month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'][::-1]
dfu = df_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
plt.pcolor(dfu, cmap='afmhot_r', edgecolors='white', linewidths=2)
plt.xticks(np.arange(0.5, len(dfu.columns), 1), dfu.columns, fontsize=7, fontfamily='serif')
plt.yticks(np.arange(0.5, len(dfu.index), 1), dfu.index, fontsize=7, fontfamily='serif')
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=8)
cbar.ax.minorticks_on()
df['date_added'] = pd.to_datetime(df['date_added'])
df['day_added'] = df['date_added'].dt.day
df['year_added'] = df['date_added'].dt.year
df['month_added'] = df['date_added'].dt.month
df['year_added'].astype(int)
df['day_added'].astype(int)
f,ax=plt.subplots(1,2,figsize=(18,8))
df['type'].value_counts().plot.pie(explode=[0,0.1],autopct='%1.1f%%',ax=ax[0],shadow=True)
ax[0].set_title('Type of Movie')
ax[0].set_ylabel('Count')
sns.countplot('type',data=df,ax=ax[1],order=df['type'].value_counts().index)
ax[1].set_title('Count of Source')
plt.show()
f,ax=plt.subplots(1,2,figsize=(18,8))
df['rating'].value_counts().plot.pie(autopct='%1.1f%%',ax=ax[0],shadow=True)
ax[0].set_title('Movie Rating')
ax[0].set_ylabel('Count')
sns.countplot('rating',data=df,ax=ax[1],order=df['rating'].value_counts().index)
ax[1].set_title('Count of Rating')
plt.show()
group_country_movies = df.groupby('country')['show_id'].count().sort_values(ascending=False).head(10)
plt.subplots(figsize=(15, 8))
group_country_movies.plot('bar', fontsize=12, color='blue')
plt.xlabel('Number of Movies', fontsize=12)
plt.ylabel('Country', fontsize=12)
plt.title('Movie count by Country', fontsize=12)
plt.ioff() | code |
48165682/cell_8 | [
"image_output_1.png"
] | import cufflinks as cf
import plotly as py
py.offline.init_notebook_mode(connected=True)
cf.go_offline() | code |
48165682/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.isnull().sum()
df = df.dropna()
df_shows = df[df['type'] == 'TV Show']
df_movies = df[df['type'] == 'Movie']
df_date = df_shows[['date_added']].dropna()
df_date['year'] = df_date['date_added'].apply(lambda x: x.split(', ')[-1])
df_date['month'] = df_date['date_added'].apply(lambda x: x.lstrip().split(' ')[0])
month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'][::-1]
dfu = df_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
plt.figure(figsize=(10, 7), dpi=200)
plt.pcolor(dfu, cmap='afmhot_r', edgecolors='white', linewidths=2)
plt.xticks(np.arange(0.5, len(dfu.columns), 1), dfu.columns, fontsize=7, fontfamily='serif')
plt.yticks(np.arange(0.5, len(dfu.index), 1), dfu.index, fontsize=7, fontfamily='serif')
plt.title('Netflix Contents Update', fontsize=12, fontfamily='calibri', fontweight='bold', position=(0.2, 1.0 + 0.02))
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=8)
cbar.ax.minorticks_on()
plt.show() | code |
48165682/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.style.use('fivethirtyeight')
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.isnull().sum()
df = df.dropna()
df_shows = df[df['type'] == 'TV Show']
df_movies = df[df['type'] == 'Movie']
df_date = df_shows[['date_added']].dropna()
df_date['year'] = df_date['date_added'].apply(lambda x: x.split(', ')[-1])
df_date['month'] = df_date['date_added'].apply(lambda x: x.lstrip().split(' ')[0])
month_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'][::-1]
dfu = df_date.groupby('year')['month'].value_counts().unstack().fillna(0)[month_order].T
plt.pcolor(dfu, cmap='afmhot_r', edgecolors='white', linewidths=2)
plt.xticks(np.arange(0.5, len(dfu.columns), 1), dfu.columns, fontsize=7, fontfamily='serif')
plt.yticks(np.arange(0.5, len(dfu.index), 1), dfu.index, fontsize=7, fontfamily='serif')
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=8)
cbar.ax.minorticks_on()
df['date_added'] = pd.to_datetime(df['date_added'])
df['day_added'] = df['date_added'].dt.day
df['year_added'] = df['date_added'].dt.year
df['month_added'] = df['date_added'].dt.month
df['year_added'].astype(int)
df['day_added'].astype(int)
f, ax = plt.subplots(1, 2, figsize=(18, 8))
df['type'].value_counts().plot.pie(explode=[0, 0.1], autopct='%1.1f%%', ax=ax[0], shadow=True)
ax[0].set_title('Type of Movie')
ax[0].set_ylabel('Count')
sns.countplot('type', data=df, ax=ax[1], order=df['type'].value_counts().index)
ax[1].set_title('Count of Source')
plt.show() | code |
48165682/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
df.head() | code |
48165682/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/netflix-shows/netflix_titles.csv')
print('Rows :', df.shape[0])
print('Columns :', df.shape[1])
print('\nFeatures :\n :', df.columns.tolist())
print('\nMissing values :', df.isnull().values.sum())
print('\nUnique values : \n', df.nunique()) | code |
72075672/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
X_full = pd.read_csv('../input/housingdataset/train.csv', index_col='Id')
X_test_full = pd.read_csv('../input/housingdataset/test.csv', index_col='Id')
X_full.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = X_full.SalePrice
X_full.drop(['SalePrice'], axis=1, inplace=True)
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X_full, y, train_size=0.8, test_size=0.2, random_state=0)
categorical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == 'object']
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
X_test = X_test_full[my_cols].copy()
X_train = pd.get_dummies(X_train)
X_valid = pd.get_dummies(X_valid)
X_test = pd.get_dummies(X_test)
X_train, X_valid = X_train.align(X_valid, join='left', axis=1)
X_train, X_test = X_train.align(X_test, join='left', axis=1)
from xgboost import XGBRegressor
from sklearn.metrics import mean_absolute_error
my_model = XGBRegressor(n_estimators=900, learning_rate=0.09)
my_model.fit(X_train, y_train)
preds = my_model_2.predict(X_valid)
mae = mean_absolute_error(preds, y_valid)
print('Mean Absolute Error:', mae) | code |
72075672/cell_2 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
X_full = pd.read_csv('../input/housingdataset/train.csv', index_col='Id')
X_test_full = pd.read_csv('../input/housingdataset/test.csv', index_col='Id')
X_full.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = X_full.SalePrice
X_full.drop(['SalePrice'], axis=1, inplace=True)
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X_full, y, train_size=0.8, test_size=0.2, random_state=0)
categorical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == 'object']
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
X_test = X_test_full[my_cols].copy()
X_train.head() | code |
122254015/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | a = 2
b = 4
c = 5
d = a + b + c
type(d)
a = 2
b = 3
c = a
a = b
b = c
print(a, b)
type(b)
type(a) | code |
122254015/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | Sales_Store_A = input('Put your sale') | code |
122254015/cell_2 | [
"text_plain_output_1.png"
] | Revenue = 1200
CostofSales = 750
CostofMarketing = 100
NetProfitMargin = int((Revenue - (CostofSales + CostofMarketing)) / Revenue * 100)
print(NetProfitMargin, 'Percentage') | code |
122254015/cell_1 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | a = 2
b = 4
c = 5
d = a + b + c
print(d)
type(d) | code |
104113435/cell_23 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.utils import resample
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['lines.color'] = 'b'
plt.rcParams['axes.grid'] = True
def denoise(data):
w = pywt.Wavelet('sym4')
maxlev = pywt.dwt_max_level(len(data), w.dec_len)
threshold = 0.04
coeffs = pywt.wavedec(data, 'sym4', level=maxlev)
for i in range(1, len(coeffs)):
coeffs[i] = pywt.threshold(coeffs[i], threshold * max(coeffs[i]))
datarec = pywt.waverec(coeffs, 'sym4')
return datarec
path = '/kaggle/input/mitbit-arrhythmia-database/mitbih_database/'
window_size = 180
maximum_counting = 10000
classes = ['N', 'L', 'R', 'A', 'V']
n_classes = len(classes)
count_classes = [0] * n_classes
X = list()
y = list()
filenames = next(os.walk(path))[2]
records = list()
annotations = list()
filenames.sort()
for f in filenames:
filename, file_extension = os.path.splitext(f)
if file_extension == '.csv':
records.append(path + filename + file_extension)
else:
annotations.append(path + filename + file_extension)
for r in range(0, len(records)):
signals = []
with open(records[r], 'rt') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
row_index = -1
for row in spamreader:
if row_index >= 0:
signals.insert(row_index, int(row[1]))
row_index += 1
signals = denoise(signals)
signals = stats.zscore(signals)
example_beat_printed = False
with open(annotations[r], 'r') as fileID:
data = fileID.readlines()
beat = list()
for d in range(1, len(data)):
splitted = data[d].split(' ')
splitted = filter(None, splitted)
next(splitted)
pos = int(next(splitted))
arrhythmia_type = next(splitted)
if arrhythmia_type in classes:
arrhythmia_index = classes.index(arrhythmia_type)
count_classes[arrhythmia_index] += 1
if window_size <= pos and pos < len(signals) - window_size:
beat = signals[pos - window_size:pos + window_size]
if r is 1 and (not example_beat_printed):
example_beat_printed = True
X.append(beat)
y.append(arrhythmia_index)
for i in range(0, len(X)):
X[i] = np.append(X[i], y[i])
X_train_df = pd.DataFrame(X)
per_class = X_train_df[X_train_df.shape[1] - 1].value_counts()
my_circle = plt.Circle((0, 0), 0.7, color='white')
p = plt.gcf()
p.gca().add_artist(my_circle)
df_1 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 1]
df_2 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 2]
df_3 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 3]
df_4 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 4]
df_0 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 0].sample(n=5000, random_state=42)
df_1_upsample = resample(df_1, replace=True, n_samples=5000, random_state=122)
df_2_upsample = resample(df_2, replace=True, n_samples=5000, random_state=123)
df_3_upsample = resample(df_3, replace=True, n_samples=5000, random_state=124)
df_4_upsample = resample(df_4, replace=True, n_samples=5000, random_state=125)
X_train_df = pd.concat([df_0, df_1_upsample, df_2_upsample, df_3_upsample, df_4_upsample])
per_class = X_train_df[X_train_df.shape[1] - 1].value_counts()
my_circle = plt.Circle((0, 0), 0.7, color='white')
p = plt.gcf()
p.gca().add_artist(my_circle)
train, test = train_test_split(X_train_df, test_size=0.2)
target_train = train[train.shape[1] - 1]
target_test = test[test.shape[1] - 1]
train_y = to_categorical(target_train)
test_y = to_categorical(target_test)
print(np.shape(train_y), np.shape(test_y)) | code |
104113435/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
from sklearn.utils import resample
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['lines.color'] = 'b'
plt.rcParams['axes.grid'] = True
def denoise(data):
w = pywt.Wavelet('sym4')
maxlev = pywt.dwt_max_level(len(data), w.dec_len)
threshold = 0.04
coeffs = pywt.wavedec(data, 'sym4', level=maxlev)
for i in range(1, len(coeffs)):
coeffs[i] = pywt.threshold(coeffs[i], threshold * max(coeffs[i]))
datarec = pywt.waverec(coeffs, 'sym4')
return datarec
path = '/kaggle/input/mitbit-arrhythmia-database/mitbih_database/'
window_size = 180
maximum_counting = 10000
classes = ['N', 'L', 'R', 'A', 'V']
n_classes = len(classes)
count_classes = [0] * n_classes
X = list()
y = list()
filenames = next(os.walk(path))[2]
records = list()
annotations = list()
filenames.sort()
for f in filenames:
filename, file_extension = os.path.splitext(f)
if file_extension == '.csv':
records.append(path + filename + file_extension)
else:
annotations.append(path + filename + file_extension)
for r in range(0, len(records)):
signals = []
with open(records[r], 'rt') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
row_index = -1
for row in spamreader:
if row_index >= 0:
signals.insert(row_index, int(row[1]))
row_index += 1
signals = denoise(signals)
signals = stats.zscore(signals)
example_beat_printed = False
with open(annotations[r], 'r') as fileID:
data = fileID.readlines()
beat = list()
for d in range(1, len(data)):
splitted = data[d].split(' ')
splitted = filter(None, splitted)
next(splitted)
pos = int(next(splitted))
arrhythmia_type = next(splitted)
if arrhythmia_type in classes:
arrhythmia_index = classes.index(arrhythmia_type)
count_classes[arrhythmia_index] += 1
if window_size <= pos and pos < len(signals) - window_size:
beat = signals[pos - window_size:pos + window_size]
if r is 1 and (not example_beat_printed):
example_beat_printed = True
X.append(beat)
y.append(arrhythmia_index)
X_train_df = pd.DataFrame(X)
per_class = X_train_df[X_train_df.shape[1] - 1].value_counts()
my_circle = plt.Circle((0, 0), 0.7, color='white')
p = plt.gcf()
p.gca().add_artist(my_circle)
df_1 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 1]
df_2 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 2]
df_3 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 3]
df_4 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 4]
df_0 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 0].sample(n=5000, random_state=42)
df_1_upsample = resample(df_1, replace=True, n_samples=5000, random_state=122)
df_2_upsample = resample(df_2, replace=True, n_samples=5000, random_state=123)
df_3_upsample = resample(df_3, replace=True, n_samples=5000, random_state=124)
df_4_upsample = resample(df_4, replace=True, n_samples=5000, random_state=125)
X_train_df = pd.concat([df_0, df_1_upsample, df_2_upsample, df_3_upsample, df_4_upsample])
per_class = X_train_df[X_train_df.shape[1] - 1].value_counts()
print(per_class)
plt.figure(figsize=(20, 10))
my_circle = plt.Circle((0, 0), 0.7, color='white')
plt.pie(per_class, labels=['N', 'L', 'R', 'A', 'V'], colors=['tab:blue', 'tab:orange', 'tab:purple', 'tab:olive', 'tab:green'], autopct='%1.1f%%')
p = plt.gcf()
p.gca().add_artist(my_circle)
plt.show() | code |
104113435/cell_15 | [
"text_plain_output_1.png"
] | from scipy import stats
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['lines.color'] = 'b'
plt.rcParams['axes.grid'] = True
def denoise(data):
w = pywt.Wavelet('sym4')
maxlev = pywt.dwt_max_level(len(data), w.dec_len)
threshold = 0.04
coeffs = pywt.wavedec(data, 'sym4', level=maxlev)
for i in range(1, len(coeffs)):
coeffs[i] = pywt.threshold(coeffs[i], threshold * max(coeffs[i]))
datarec = pywt.waverec(coeffs, 'sym4')
return datarec
path = '/kaggle/input/mitbit-arrhythmia-database/mitbih_database/'
window_size = 180
maximum_counting = 10000
classes = ['N', 'L', 'R', 'A', 'V']
n_classes = len(classes)
count_classes = [0] * n_classes
X = list()
y = list()
filenames = next(os.walk(path))[2]
records = list()
annotations = list()
filenames.sort()
for f in filenames:
filename, file_extension = os.path.splitext(f)
if file_extension == '.csv':
records.append(path + filename + file_extension)
else:
annotations.append(path + filename + file_extension)
for r in range(0, len(records)):
signals = []
with open(records[r], 'rt') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
row_index = -1
for row in spamreader:
if row_index >= 0:
signals.insert(row_index, int(row[1]))
row_index += 1
signals = denoise(signals)
signals = stats.zscore(signals)
example_beat_printed = False
with open(annotations[r], 'r') as fileID:
data = fileID.readlines()
beat = list()
for d in range(1, len(data)):
splitted = data[d].split(' ')
splitted = filter(None, splitted)
next(splitted)
pos = int(next(splitted))
arrhythmia_type = next(splitted)
if arrhythmia_type in classes:
arrhythmia_index = classes.index(arrhythmia_type)
count_classes[arrhythmia_index] += 1
if window_size <= pos and pos < len(signals) - window_size:
beat = signals[pos - window_size:pos + window_size]
if r is 1 and (not example_beat_printed):
example_beat_printed = True
X.append(beat)
y.append(arrhythmia_index)
for i in range(0, len(X)):
X[i] = np.append(X[i], y[i])
print(np.shape(X)) | code |
104113435/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['lines.color'] = 'b'
plt.rcParams['axes.grid'] = True
def denoise(data):
w = pywt.Wavelet('sym4')
maxlev = pywt.dwt_max_level(len(data), w.dec_len)
threshold = 0.04
coeffs = pywt.wavedec(data, 'sym4', level=maxlev)
for i in range(1, len(coeffs)):
coeffs[i] = pywt.threshold(coeffs[i], threshold * max(coeffs[i]))
datarec = pywt.waverec(coeffs, 'sym4')
return datarec
path = '/kaggle/input/mitbit-arrhythmia-database/mitbih_database/'
window_size = 180
maximum_counting = 10000
classes = ['N', 'L', 'R', 'A', 'V']
n_classes = len(classes)
count_classes = [0] * n_classes
X = list()
y = list()
filenames = next(os.walk(path))[2]
records = list()
annotations = list()
filenames.sort()
for f in filenames:
filename, file_extension = os.path.splitext(f)
if file_extension == '.csv':
records.append(path + filename + file_extension)
else:
annotations.append(path + filename + file_extension)
for r in range(0, len(records)):
signals = []
with open(records[r], 'rt') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
row_index = -1
for row in spamreader:
if row_index >= 0:
signals.insert(row_index, int(row[1]))
row_index += 1
signals = denoise(signals)
signals = stats.zscore(signals)
example_beat_printed = False
with open(annotations[r], 'r') as fileID:
data = fileID.readlines()
beat = list()
for d in range(1, len(data)):
splitted = data[d].split(' ')
splitted = filter(None, splitted)
next(splitted)
pos = int(next(splitted))
arrhythmia_type = next(splitted)
if arrhythmia_type in classes:
arrhythmia_index = classes.index(arrhythmia_type)
count_classes[arrhythmia_index] += 1
if window_size <= pos and pos < len(signals) - window_size:
beat = signals[pos - window_size:pos + window_size]
if r is 1 and (not example_beat_printed):
example_beat_printed = True
X.append(beat)
y.append(arrhythmia_index)
X_train_df = pd.DataFrame(X)
per_class = X_train_df[X_train_df.shape[1] - 1].value_counts()
print(per_class)
plt.figure(figsize=(20, 10))
my_circle = plt.Circle((0, 0), 0.7, color='white')
plt.pie(per_class, labels=['N', 'L', 'R', 'A', 'V'], colors=['tab:blue', 'tab:orange', 'tab:purple', 'tab:olive', 'tab:green'], autopct='%1.1f%%')
p = plt.gcf()
p.gca().add_artist(my_circle)
plt.show() | code |
104113435/cell_14 | [
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy import stats
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['lines.color'] = 'b'
plt.rcParams['axes.grid'] = True
def denoise(data):
w = pywt.Wavelet('sym4')
maxlev = pywt.dwt_max_level(len(data), w.dec_len)
threshold = 0.04
coeffs = pywt.wavedec(data, 'sym4', level=maxlev)
for i in range(1, len(coeffs)):
coeffs[i] = pywt.threshold(coeffs[i], threshold * max(coeffs[i]))
datarec = pywt.waverec(coeffs, 'sym4')
return datarec
path = '/kaggle/input/mitbit-arrhythmia-database/mitbih_database/'
window_size = 180
maximum_counting = 10000
classes = ['N', 'L', 'R', 'A', 'V']
n_classes = len(classes)
count_classes = [0] * n_classes
X = list()
y = list()
filenames = next(os.walk(path))[2]
records = list()
annotations = list()
filenames.sort()
for f in filenames:
filename, file_extension = os.path.splitext(f)
if file_extension == '.csv':
records.append(path + filename + file_extension)
else:
annotations.append(path + filename + file_extension)
for r in range(0, len(records)):
signals = []
with open(records[r], 'rt') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
row_index = -1
for row in spamreader:
if row_index >= 0:
signals.insert(row_index, int(row[1]))
row_index += 1
if r is 1:
plt.title(records[1] + ' Wave')
plt.plot(signals[0:700])
plt.show()
signals = denoise(signals)
if r is 1:
plt.title(records[1] + ' wave after denoised')
plt.plot(signals[0:700])
plt.show()
signals = stats.zscore(signals)
if r is 1:
plt.title(records[1] + ' wave after z-score normalization ')
plt.plot(signals[0:700])
plt.show()
example_beat_printed = False
with open(annotations[r], 'r') as fileID:
data = fileID.readlines()
beat = list()
for d in range(1, len(data)):
splitted = data[d].split(' ')
splitted = filter(None, splitted)
next(splitted)
pos = int(next(splitted))
arrhythmia_type = next(splitted)
if arrhythmia_type in classes:
arrhythmia_index = classes.index(arrhythmia_type)
count_classes[arrhythmia_index] += 1
if window_size <= pos and pos < len(signals) - window_size:
beat = signals[pos - window_size:pos + window_size]
if r is 1 and (not example_beat_printed):
plt.title('A Beat from ' + records[1] + ' Wave')
plt.plot(beat)
plt.show()
example_beat_printed = True
X.append(beat)
y.append(arrhythmia_index)
print(np.shape(X), np.shape(y)) | code |
104113435/cell_22 | [
"text_plain_output_1.png"
] | from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.utils import resample
import csv
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pywt
plt.rcParams['figure.figsize'] = (30, 6)
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['lines.color'] = 'b'
plt.rcParams['axes.grid'] = True
def denoise(data):
w = pywt.Wavelet('sym4')
maxlev = pywt.dwt_max_level(len(data), w.dec_len)
threshold = 0.04
coeffs = pywt.wavedec(data, 'sym4', level=maxlev)
for i in range(1, len(coeffs)):
coeffs[i] = pywt.threshold(coeffs[i], threshold * max(coeffs[i]))
datarec = pywt.waverec(coeffs, 'sym4')
return datarec
path = '/kaggle/input/mitbit-arrhythmia-database/mitbih_database/'
window_size = 180
maximum_counting = 10000
classes = ['N', 'L', 'R', 'A', 'V']
n_classes = len(classes)
count_classes = [0] * n_classes
X = list()
y = list()
filenames = next(os.walk(path))[2]
records = list()
annotations = list()
filenames.sort()
for f in filenames:
filename, file_extension = os.path.splitext(f)
if file_extension == '.csv':
records.append(path + filename + file_extension)
else:
annotations.append(path + filename + file_extension)
for r in range(0, len(records)):
signals = []
with open(records[r], 'rt') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
row_index = -1
for row in spamreader:
if row_index >= 0:
signals.insert(row_index, int(row[1]))
row_index += 1
signals = denoise(signals)
signals = stats.zscore(signals)
example_beat_printed = False
with open(annotations[r], 'r') as fileID:
data = fileID.readlines()
beat = list()
for d in range(1, len(data)):
splitted = data[d].split(' ')
splitted = filter(None, splitted)
next(splitted)
pos = int(next(splitted))
arrhythmia_type = next(splitted)
if arrhythmia_type in classes:
arrhythmia_index = classes.index(arrhythmia_type)
count_classes[arrhythmia_index] += 1
if window_size <= pos and pos < len(signals) - window_size:
beat = signals[pos - window_size:pos + window_size]
if r is 1 and (not example_beat_printed):
example_beat_printed = True
X.append(beat)
y.append(arrhythmia_index)
for i in range(0, len(X)):
X[i] = np.append(X[i], y[i])
X_train_df = pd.DataFrame(X)
per_class = X_train_df[X_train_df.shape[1] - 1].value_counts()
my_circle = plt.Circle((0, 0), 0.7, color='white')
p = plt.gcf()
p.gca().add_artist(my_circle)
df_1 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 1]
df_2 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 2]
df_3 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 3]
df_4 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 4]
df_0 = X_train_df[X_train_df[X_train_df.shape[1] - 1] == 0].sample(n=5000, random_state=42)
df_1_upsample = resample(df_1, replace=True, n_samples=5000, random_state=122)
df_2_upsample = resample(df_2, replace=True, n_samples=5000, random_state=123)
df_3_upsample = resample(df_3, replace=True, n_samples=5000, random_state=124)
df_4_upsample = resample(df_4, replace=True, n_samples=5000, random_state=125)
X_train_df = pd.concat([df_0, df_1_upsample, df_2_upsample, df_3_upsample, df_4_upsample])
per_class = X_train_df[X_train_df.shape[1] - 1].value_counts()
my_circle = plt.Circle((0, 0), 0.7, color='white')
p = plt.gcf()
p.gca().add_artist(my_circle)
train, test = train_test_split(X_train_df, test_size=0.2)
print('X_train : ', np.shape(train))
print('X_test : ', np.shape(test)) | code |
129014767/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1, 2, 3, 4, 5])
pd.DataFrame([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'], columns=['Number'])
list = [['a', 25], ['b', 30], ['c', 26], ['d', 22]]
pd.DataFrame(list, columns=['Alphabet', 'Number'])
dict = {'Alphabet': ['a', 'b', 'c', 'd', 'e'], 'Number': [1, 2, 3, 4, 5]}
pd.DataFrame(dict) | code |
129014767/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
pd.Series([1, 2, 3, 4, 5]) | code |
129014767/cell_6 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series | code |
129014767/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1, 2, 3, 4, 5])
pd.DataFrame([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'], columns=['Number']) | code |
129014767/cell_7 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series | code |
129014767/cell_8 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series | code |
129014767/cell_15 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1, 2, 3, 4, 5])
pd.DataFrame([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'], columns=['Number'])
list = [['a', 25], ['b', 30], ['c', 26], ['d', 22]]
pd.DataFrame(list, columns=['Alphabet', 'Number'])
dict = {'Alphabet': ['a', 'b', 'c', 'd', 'e'], 'Number': [1, 2, 3, 4, 5]}
pd.DataFrame(dict)
array = np.array([1, 2, 3, 4, 5])
pd.DataFrame(array, columns=['Number'])
array = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25]])
pd.DataFrame(array, columns=['Number', 'Square']) | code |
129014767/cell_16 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1, 2, 3, 4, 5])
pd.DataFrame([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'], columns=['Number'])
list = [['a', 25], ['b', 30], ['c', 26], ['d', 22]]
pd.DataFrame(list, columns=['Alphabet', 'Number'])
dict = {'Alphabet': ['a', 'b', 'c', 'd', 'e'], 'Number': [1, 2, 3, 4, 5]}
pd.DataFrame(dict)
array = np.array([1, 2, 3, 4, 5])
pd.DataFrame(array, columns=['Number'])
array = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25]])
pd.DataFrame(array, columns=['Number', 'Square'])
pd.DataFrame(range(5)) | code |
129014767/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1, 2, 3, 4, 5])
pd.DataFrame([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'], columns=['Number'])
list = [['a', 25], ['b', 30], ['c', 26], ['d', 22]]
pd.DataFrame(list, columns=['Alphabet', 'Number'])
dict = {'Alphabet': ['a', 'b', 'c', 'd', 'e'], 'Number': [1, 2, 3, 4, 5]}
pd.DataFrame(dict)
array = np.array([1, 2, 3, 4, 5])
pd.DataFrame(array, columns=['Number']) | code |
129014767/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1, 2, 3, 4, 5]) | code |
129014767/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series
array = np.array([10, 20, 30, 40, 50])
series = pd.Series(array)
series
dict = {'a': 10, 'b': 20, 'd': 30}
series = pd.Series(dict)
series
series = pd.Series(range(15))
series
pd.DataFrame([1, 2, 3, 4, 5])
pd.DataFrame([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'], columns=['Number'])
list = [['a', 25], ['b', 30], ['c', 26], ['d', 22]]
pd.DataFrame(list, columns=['Alphabet', 'Number']) | code |
129014767/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
pd.Series([1, 2, 3, 4, 5])
list = [1, 2, 3, 4, 5]
series = pd.Series(list)
series | code |
105181512/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
df = pd.read_csv('haberman.csv')
df.head() | code |
17105960/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_feature
train['Activity']
train.Activity.sample(5)
feature_cols = train.columns[:-1]
correlated_values = train[feature_cols].corr()
correlated_values = correlated_values.stack().to_frame().reset_index().rename(columns={'level_0': 'Feature_1', 'level_1': 'Feature_2', 0: 'Correlations'})
correlated_values['abs_correlation'] = correlated_values.Correlations.abs()
train_fields = correlated_values.sort_values('Correlations', ascending=False).query('abs_correlation>0.8')
train_fields.sample(5) | code |
17105960/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
print(train.isnull().values.any())
print(test.isnull().values.any()) | code |
17105960/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
print(train.dtypes.value_counts())
print(test.dtypes.value_counts()) | code |
17105960/cell_2 | [
"text_html_output_1.png"
] | import os
import pandas as pd
print(os.listdir('../input'))
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv') | code |
17105960/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_feature
train['Activity']
train.Activity.sample(5)
feature_cols = train.columns[:-1]
correlated_values = train[feature_cols].corr()
correlated_values = correlated_values.stack().to_frame().reset_index().rename(columns={'level_0': 'Feature_1', 'level_1': 'Feature_2', 0: 'Correlations'})
correlated_values.head() | code |
17105960/cell_7 | [
"text_html_output_1.png"
] | import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
train.describe() | code |
17105960/cell_8 | [
"text_html_output_1.png"
] | import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_feature
train['Activity'] | code |
17105960/cell_15 | [
"text_html_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_feature
train['Activity']
train.Activity.sample(5)
feature_cols = train.columns[:-1]
correlated_values = train[feature_cols].corr()
correlated_values = correlated_values.stack().to_frame().reset_index().rename(columns={'level_0': 'Feature_1', 'level_1': 'Feature_2', 0: 'Correlations'})
split_data = StratifiedShuffleSplit(n_splits=1, test_size=0.3, random_state=42)
train_idx, val_idx = next(split_data.split(train[feature_cols], train.Activity))
x_train = train.loc[train_idx, feature_cols]
y_train = train.loc[train_idx, 'Activity']
x_val = train.loc[val_idx, feature_cols]
y_val = train.loc[val_idx, 'Activity']
print(y_train.value_counts(normalize=True))
print(y_val.value_counts(normalize=True)) | code |
17105960/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.model_selection import StratifiedShuffleSplit
import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_feature
train['Activity']
train.Activity.sample(5)
feature_cols = train.columns[:-1]
correlated_values = train[feature_cols].corr()
correlated_values = correlated_values.stack().to_frame().reset_index().rename(columns={'level_0': 'Feature_1', 'level_1': 'Feature_2', 0: 'Correlations'})
split_data = StratifiedShuffleSplit(n_splits=1, test_size=0.3, random_state=42)
train_idx, val_idx = next(split_data.split(train[feature_cols], train.Activity))
x_train = train.loc[train_idx, feature_cols]
y_train = train.loc[train_idx, 'Activity']
x_val = train.loc[val_idx, feature_cols]
y_val = train.loc[val_idx, 'Activity']
lr_l2 = LogisticRegressionCV(cv=4, penalty='l2', max_iter=1000, n_jobs=-1)
lr_l2 = lr_l2.fit(x_train, y_train) | code |
17105960/cell_3 | [
"text_html_output_1.png"
] | import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
17105960/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.model_selection import StratifiedShuffleSplit
import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_feature
train['Activity']
train.Activity.sample(5)
feature_cols = train.columns[:-1]
correlated_values = train[feature_cols].corr()
correlated_values = correlated_values.stack().to_frame().reset_index().rename(columns={'level_0': 'Feature_1', 'level_1': 'Feature_2', 0: 'Correlations'})
split_data = StratifiedShuffleSplit(n_splits=1, test_size=0.3, random_state=42)
train_idx, val_idx = next(split_data.split(train[feature_cols], train.Activity))
x_train = train.loc[train_idx, feature_cols]
y_train = train.loc[train_idx, 'Activity']
x_val = train.loc[val_idx, feature_cols]
y_val = train.loc[val_idx, 'Activity']
lr_l2 = LogisticRegressionCV(cv=4, penalty='l2', max_iter=1000, n_jobs=-1)
lr_l2 = lr_l2.fit(x_train, y_train)
y_predict = list()
y_proba = list()
labels = ['lr_l2']
models = [lr_l2]
for lab, mod in zip(labels, models):
y_predict.append(pd.Series(mod.predict(x_val), name=lab))
y_proba.append(pd.Series(mod.predict_proba(x_val).max(axis=1), name=lab))
y_predict = pd.concat(y_predict, axis=1)
y_proba = pd.concat(y_proba, axis=1)
y_predict.head() | code |
17105960/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_feature
train['Activity']
train.Activity.sample(5) | code |
17105960/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.drop('subject', axis=1, inplace=True)
test.drop('subject', axis=1, inplace=True)
object_feature = train.dtypes == np.object
object_feature = train.columns[object_feature]
object_feature
train['Activity']
train.Activity.sample(5)
feature_cols = train.columns[:-1]
correlated_values = train[feature_cols].corr()
correlated_values = correlated_values.stack().to_frame().reset_index().rename(columns={'level_0': 'Feature_1', 'level_1': 'Feature_2', 0: 'Correlations'})
correlated_values['abs_correlation'] = correlated_values.Correlations.abs()
correlated_values.head() | code |
104126055/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, Y_train)
Y_pred = classifier.predict(X_test)
print('Accuracy = ', accuracy_score(Y_test, Y_pred) * 100, '%') | code |
104126055/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset['Sex'].unique() | code |
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